We consider a class of visual analogical reasoning problems that involve
discovering the sequence of transformations by which pairs of input/output images are related, so
as to analogously transform future inputs. This program synthesis task can be easily solved
via symbolic search. Using a variation of the `neural analogical reasoning'
approach of [1]}, we instead search for a sequence
of elementary neural network transformations that manipulate distributed representations
derived from a symbolic space, to which input images are directly encoded.
We evaluate the extent to which our `neural reasoning' approach generalizes for images with unseen shapes and positions.
